Analyzing the Environmental, Social, and Governance (ESG) performance of listed companies provides valuable insights. However, restatements of past indicators, mismatches in time between financial and non-financial reporting, and redefinitions of ESG scores and categories create a burden for interpreting trends.
Some changes in the data occur at the company level. ESG data disclosed in sustainability reports are frequently restated. Some companies rectify their environmental figures from past years regularly. This is due to changing perceptions of how ESG data should be reported and to the absence of mandatory standards as exist in financial reporting. Along with this comes another issue—the publication dates of sustainability reports and financial reports are not necessarily the same, which is relevant for the next step in the data processing cycle.
An increasing number of ESG data providers pick up company data and make them accessible at different intervals according to their work and update rhythm. The ESG data are then available in a terminal environment, a feed, or a database. But it is not always clear to data providers or other interested parties when company ESG data can be expected, and there may be delays in processing. In addition, policies dictating how company restatements of past years are to be considered may vary from one data provider to another.
On the next level, many ESG company ratings and scores, which are based on the evaluation of company data by various methods, are volatile by nature, as companies are assessed relative to others and, e.g., grouped in percentiles. Again, the update cycles for scores and ratings vary. Besides, methodology changes have occurred over the years, some of which make it difficult to compare current with past data.
Depending on the type of question being asked in the work with ESG data, these aspects must be considered. Special attention is required in the context of time series analysis and back testing – an interesting case is documented here.
Overall, the quality of ESG analyses largely depends on data quality, data access, and the ability to digest the manifold information from different sources. In the context of changing ESG history, our specific workflow management helps to handle data efficiently and to extract meaningful information. Furthermore, the analysis of such changes over time is an interesting field on its own.